발간년도 : [2017]
| 논문정보 |
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| 논문명(한글) |
[Vol.12, No.1] A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification |
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| 논문투고자 |
Yoon-Sung Kim, Ho-Chang Lee, Seok Kee Lee, Do-Gil Lee, Han-Gook Kim, You-Eil Kim |
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| 논문내용 |
Today’s companies are in an environment where they have to survive in ever-increasing competition in the industry, by constantly identifying changes and trends in their industries and by periodically reflecting them in their policies and product development. For this purpose, one of the tasks that should be carried out is the analysis of industrial information. Most companies acquire industry analytical information at the cost of a large amount of time, manpower or, with the help of external professional analysts. However, since this conventional method is a somewhat heuristic and qualitative approach. The quality of these analysis results are different each time. A huge amount of industry related information is produced online in real time and when the information is reflected in the analysis as much as possible, it is required to introduce a new analytical method. In this paper, we propose a text mining methodology that extracts information from large amount of source data and automatically classifies it into each category of industry analysis framework. By constructing a sentence classifier using feature selection technique based on machine learning method, information that can be classified by indicators of universally used industry analysis framework is collected in sentence form. We performed PEST and polarity analysis by using our system and evaluated the classification accuracy of the proposed system through experiments. |
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